Research Area:  Machine Learning
In the intelligent transportation system, the session data usually represents the users demand. However, the traditional approaches only focus on the sequence information or the last item clicked by the user, which cannot fully represent user preferences. To address this issue, this paper proposes an Multi-aspect Aware Session-based Recommendation (MASR) model for intelligent transportation services, which comprehensively considers the users personalized behavior from multiple aspects. In addition, it developed a concise and efficient transformer-style self-attention to analyze the sequence information of the current session, for accurately grasping the users intention. Finally, the experimental results show that MASR is available to improve user satisfaction with more accurate and rapid recommendations, and reduce the number of user operations to decrease the safety risk during the transportation service.
Author(s) Name:  Yin Zhang; Yujie Li; Ranran Wang; M. Shamim Hossain; Huimin Lu
Journal name:  IEEE Transactions on Intelligent Transportation Systems
Publisher name:  IEEE
Volume Information:  ( Volume: 22, Issue: 7, July 2021) Page(s): 4696 - 4705
Paper Link:   https://ieeexplore.ieee.org/abstract/document/9093954